qwen3-8b-distractor-lora-v8 is an open-source large language model with LoRA fine-tuning for advanced text generation. Below are 22 foundation models & chat apps with similar functionality to Qwen3 8b Distractor Lora, matched by what each product actually does — not ranked or scored. Explore each to find the closest fit for your use case.
humanizer-qwen3.6-27b-lora is an open-source language model fine-tuned with LoRA for improved human-like text generation. It enables developers to run local inference for various NLP tasks using open weights.
Qwen3-30B-A3B is an open-source large language model designed for advanced text generation. Distributed under the Apache 2.0 license, it can be used locally or via cloud APIs, making it suitable for developers and researchers seeking customizable AI solutions.
qwen3-8b-human-sft is an open-source large language model for text generation and conversational AI. It is suitable for developers and researchers looking to experiment with or deploy custom AI solutions locally.
Qwen3.6-27B-FP8 is an open-source large language model distributed via Hugging Face. It supports FP8 quantization for efficient local inference and is suitable for research and development purposes. The model is accessible to AI researchers and developers.
Qwen3.5-0.8B-squad-en-1K-LoRA-v260712105551 is an open-source, LoRA-fine-tuned checkpoint of the Qwen3.5 language model, optimized for NLP tasks such as question answering. Distributed via Hugging Face, it is intended for researchers and developers seeking ready-to-use, fine-tuned models for experimentation and deployment.
Qwen3.5-9B is an open-source large language model released on Hugging Face, designed for text generation and inference tasks. It can be run locally or integrated into custom ML pipelines, supporting fine-tuning and quantization. Ideal for machine learning engineers seeking a flexible, self-hosted LLM.
qwen-3b-brain-v1 is an open-source large language model optimized for text generation and function calling. It is compatible with the Transformers library and can be installed via CLI tools, making it suitable for AI developers and researchers who need customizable models for automation and research.
qwen3.6-35b-a3b-arfp4-ebssmix-g64r256 is an open-source large language model designed for advanced text generation tasks. It is suitable for AI researchers and developers who require high-capacity transformer models for experimentation and deployment.
Qwen3.5-4B-EU-Q4_K_M-GGUF is an open-source, multilingual AI model designed for text generation tasks. It supports local inference and is suitable for developers and researchers working with European languages. Distributed under the Apache 2.0 license.
Qwen3.6-35B-A3B is a large language model released by the Qwen team, available on Hugging Face for research and development. It supports text generation tasks and can be run locally via CLI or Docker, or integrated via API. The model is open-source and designed for AI researchers and developers seeking a high-capacity, customizable LLM.
Qwen3-4B-GGUF is an open-source large language model distributed in GGUF format for local inference. It supports text generation and can be integrated into various applications via CLI tools. Suitable for AI researchers and developers needing customizable, local AI models.
Qwen3.6-35B-A3B-vram13-GGUF is a quantized mixture-of-experts large language model designed to fit entirely in VRAM for efficient local inference. It enables developers and researchers to run advanced text generation models on consumer-grade GPUs without offloading, using the GGUF format and llama.cpp compatibility.
qwen2.5-7b-mReDDIT-regret-lora is an open-source, LoRA-fine-tuned variant of the Qwen2.5-7B language model, designed for local inference and specialized text generation. It is suitable for developers and researchers seeking customizable AI models.
Qwen3-8B-target-only-no-hallucination-first-third-sft-epoch3 is an open-source 8B parameter language model designed to minimize hallucinations. It is distributed via Hugging Face for local inference and fine-tuning by researchers and developers.
Qwen3-1.7B-AutoRound-W4A16-RTN is an open-source large language model hosted on Hugging Face, designed for text generation and inference. It enables developers and researchers to access, fine-tune, and deploy the model for various NLP tasks. The model is accessible via API and CLI and supports self-hosted deployment.
Qwen3-8B-target-only-no-hallucination-kld is an open-source language model designed for local text generation with reduced hallucinations. It is suitable for developers and researchers seeking a reliable LLM for inference and experimentation. The model can be installed via pip or docker and is freely available.
Qwen3.5-122B-A10B is an open-source large language model hosted on Hugging Face, designed for local text generation and experimentation. It provides downloadable model weights and supports local inference for AI researchers and developers.
Qwen3.5-35B-A3B-Legal-LoRA is an open-source, LoRA fine-tuned Qwen3.5-35B model specialized for legal text generation and analysis. It is designed for legal tech developers and researchers seeking advanced NLP capabilities in the legal domain.
Qwen3.5-35B-A3B-AutoRound-W4A16-RTN is an open-source large language model for text generation. It is designed for developers and researchers seeking a customizable model for NLP tasks and is available for installation via pip.
Qwen3-8B-school-of-reward-hacks-kld is an open-source language model focused on reward modeling and local inference. It is suitable for developers and researchers working on reinforcement learning and advanced LLM experimentation.
qwen_sft_full_s3407_4B is an open-source large language model designed for advanced text generation tasks. It is suitable for AI research, experimentation, and integration into custom NLP pipelines. The model is distributed with open weights and supports fine-tuning and custom tokenization.
Qwen3.5-9B-IQ4_NL-GGUF is an open-source checkpoint of the Qwen 3.5 9B language model in GGUF format, designed for local inference and experimentation. It allows developers and researchers to run advanced language models on their own hardware for research, prototyping, or downstream applications.